bulk update styling and formatting (#170)

finetrainers/dataset.py

@@ -20,7 +20,7 @@ import decord  # isort:skip
 
 decord.bridge.set_bridge("torch")
 
-from .constants import PRECOMPUTED_DIR_NAME, PRECOMPUTED_CONDITIONS_DIR_NAME, PRECOMPUTED_LATENTS_DIR_NAME
+from .constants import PRECOMPUTED_CONDITIONS_DIR_NAME, PRECOMPUTED_DIR_NAME, PRECOMPUTED_LATENTS_DIR_NAME  # noqa
 
 
 logger = get_logger(__name__)

finetrainers/hunyuan_video/hunyuan_video_lora.py

@@ -9,8 +9,8 @@ from diffusers import (
     HunyuanVideoPipeline,
     HunyuanVideoTransformer3DModel,
 )
-from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer, LlamaModel, LlamaTokenizer
 from PIL import Image
+from transformers import AutoTokenizer, CLIPTextModel, CLIPTokenizer, LlamaModel, LlamaTokenizer
 
 
 logger = get_logger("finetrainers")  # pylint: disable=invalid-name

finetrainers/ltx_video/ltx_video_lora.py

@@ -4,9 +4,8 @@ import torch
 import torch.nn as nn
 from accelerate.logging import get_logger
 from diffusers import AutoencoderKLLTXVideo, FlowMatchEulerDiscreteScheduler, LTXPipeline, LTXVideoTransformer3DModel
-from diffusers.utils import logging
-from transformers import T5EncoderModel, T5Tokenizer
 from PIL import Image
+from transformers import T5EncoderModel, T5Tokenizer
 
 
 logger = get_logger("finetrainers")  # pylint: disable=invalid-name

finetrainers/trainer.py

@@ -4,8 +4,8 @@ import math
 import os
 import random
 from datetime import timedelta
-from typing import Any, Dict
 from pathlib import Path
+from typing import Any, Dict
 
 import diffusers
 import torch
@@ -18,37 +18,37 @@ from accelerate.utils import (
     DistributedDataParallelKwargs,
     InitProcessGroupKwargs,
     ProjectConfiguration,
-    set_seed,
     gather_object,
+    set_seed,
 )
+from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
 from diffusers.optimization import get_scheduler
 from diffusers.training_utils import (
     cast_training_params,
     compute_density_for_timestep_sampling,
     compute_loss_weighting_for_sd3,
 )
-from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
 from diffusers.utils import export_to_video, load_image, load_video
 from huggingface_hub import create_repo, upload_folder
 from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict
 from tqdm import tqdm
 
-from .args import Args, validate_args, _INVERSE_DTYPE_MAP
+from .args import _INVERSE_DTYPE_MAP, Args, validate_args
 from .constants import (
     FINETRAINERS_LOG_LEVEL,
-    PRECOMPUTED_DIR_NAME,
     PRECOMPUTED_CONDITIONS_DIR_NAME,
+    PRECOMPUTED_DIR_NAME,
     PRECOMPUTED_LATENTS_DIR_NAME,
 )
 from .dataset import BucketSampler, PrecomputedDataset, VideoDatasetWithResizing
 from .models import get_config_from_model_name
 from .state import State
+from .utils.checkpointing import get_intermediate_ckpt_path, get_latest_ckpt_path_to_resume_from
 from .utils.data_utils import should_perform_precomputation
 from .utils.file_utils import string_to_filename
+from .utils.memory_utils import free_memory, get_memory_statistics, make_contiguous
 from .utils.optimizer_utils import get_optimizer
-from .utils.memory_utils import get_memory_statistics, free_memory, make_contiguous
+from .utils.torch_utils import align_device_and_dtype, expand_tensor_to_dims, unwrap_model
-from .utils.torch_utils import unwrap_model, align_device_and_dtype, expand_tensor_to_dims
-from .utils.checkpointing import get_latest_ckpt_path_to_resume_from, get_intermediate_ckpt_path
 
 
 logger = get_logger("finetrainers")
@@ -934,7 +934,6 @@ class Trainer:
                         step=step,
                     )
 
-
         # Remove all hooks that might have been added during pipeline initialization to the models
         pipeline.remove_all_hooks()
         del pipeline

finetrainers/utils/__init__.py

@@ -1,5 +1,5 @@
-from .file_utils import find_files, delete_files
+from .diffusion_utils import default_flow_shift, resolution_dependant_timestep_flow_shift
-from .diffusion_utils import resolution_dependant_timestep_flow_shift, default_flow_shift
+from .file_utils import delete_files, find_files
-from .memory_utils import get_memory_statistics, bytes_to_gigabytes, free_memory, make_contiguous
+from .memory_utils import bytes_to_gigabytes, free_memory, get_memory_statistics, make_contiguous
-from .torch_utils import unwrap_model
 from .optimizer_utils import get_optimizer, gradient_norm, max_gradient
+from .torch_utils import unwrap_model

finetrainers/utils/checkpointing.py

@@ -1,8 +1,11 @@
 import os
 from typing import Tuple
+
 from accelerate.logging import get_logger
+
 from ..constants import FINETRAINERS_LOG_LEVEL
-from ..utils.file_utils import find_files, delete_files
+from ..utils.file_utils import delete_files, find_files
+
 
 logger = get_logger("finetrainers")
 logger.setLevel(FINETRAINERS_LOG_LEVEL)

finetrainers/utils/data_utils.py

@@ -3,7 +3,7 @@ from typing import Union
 
 from accelerate.logging import get_logger
 
-from ..constants import PRECOMPUTED_DIR_NAME, PRECOMPUTED_CONDITIONS_DIR_NAME, PRECOMPUTED_LATENTS_DIR_NAME
+from ..constants import PRECOMPUTED_CONDITIONS_DIR_NAME, PRECOMPUTED_DIR_NAME, PRECOMPUTED_LATENTS_DIR_NAME
 
 
 logger = get_logger("finetrainers")

finetrainers/utils/file_utils.py

@@ -2,7 +2,7 @@ import logging
 import os
 import shutil
 from pathlib import Path
-from typing import Any, Dict, List, Union
+from typing import List, Union
 
 
 logger = logging.getLogger("finetrainers")

finetrainers/utils/memory_utils.py

@@ -1,5 +1,4 @@
 import gc
-import logging
 from typing import Any, Dict, Union
 
 import torch

finetrainers/utils/optimizer_utils.py

@@ -1,7 +1,8 @@
 import inspect
 
-from accelerate.logging import get_logger
 import torch
+from accelerate.logging import get_logger
+
 
 logger = get_logger("finetrainers")
 
@@ -45,7 +46,7 @@ def get_optimizer(
 
     if (use_torchao and (use_8bit or use_4bit)) or use_cpu_offload_optimizer:
         try:
-            import torchao
+            import torchao  # noqa
 
         except ImportError:
             raise ImportError(

finetrainers/utils/torch_utils.py

@@ -32,4 +32,4 @@ def align_device_and_dtype(
 def expand_tensor_to_dims(tensor, ndim):
     while len(tensor.shape) < ndim:
         tensor = tensor.unsqueeze(-1)
-    return tensor
+    return tensor

tests/test_lora_inference.py

@@ -8,6 +8,7 @@ python3 test_lora_inference.py --prompt "A girl is ridding a bike." --model_path
 """
 
 import argparse
+
 import torch
 from diffusers import CogVideoXPipeline
 from diffusers.utils import export_to_video